About Me
My background, expertise, and professional journey
Who I Am
I'm Blake Sonnier, an AI/ML Engineer with 7+ years of experience delivering data-driven solutions in healthcare and healthtech. I specialize in deploying deep learning models for diagnostic imaging, real-time patient monitoring, and risk prediction systems.
My expertise spans computer vision, natural language processing, and time series analysis, with a strong foundation in Python, PyTorch, and cloud-native technologies. I've successfully bridged clinical insight with scalable AI systems throughout my career, making complex healthcare data accessible and actionable.
As a Full Stack AI/ML Engineer at Invene, I lead the development of cutting-edge healthcare applications including EEG cognitive state classification and MRI segmentation pipelines. I'm particularly passionate about optimizing deep learning models for edge deployment in clinical settings, where real-time performance and reliability are critical.
Based in Lumberton, Texas, I'm always exploring new opportunities to apply my technical expertise to solve meaningful healthcare challenges.
Education
Master of Science in Computer Science
The University of Texas at Austin
Focused on Artificial Intelligence and Machine Learning. Capstone: Diabetic retinopathy detection using CNNs on retinal images. Courses included ML, DL, NLP, and Reinforcement Learning with Prof. Peter Stone. Graduate Research Fellow in AI for Population Health.
Bachelor of Science in Computer Science
Lamar University
Dean's List (4 semesters). Participated in ACM Student Chapter, worked as Data Mining Lab Assistant, and was a Hackathon Finalist. Final-year project focused on building a recommendation system using collaborative filtering.
Key Skills
Languages
ML/Data Science
Frameworks/Libraries
Cloud/DevOps
Healthcare Tech
Software Engineering
Professional Strengths
- •Deep expertise in deep learning model optimization for healthcare applications, including quantization, pruning, and model compression
- •Experience with the complete machine learning lifecycle from data preparation to production deployment
- •Ability to bridge technical and clinical domains, translating healthcare needs into effective AI solutions
- •Skilled in developing interpretable AI solutions with explainable outputs for clinical and regulatory requirements
- •Strong communication skills with experience presenting complex technical concepts to diverse stakeholders